Person: ARĞA, KAZIM YALÇIN
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ARĞA
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KAZIM YALÇIN
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Publication Metadata only The Stimulatory Effect of Mannitol on Levan Biosynthesis: Lessons from Metabolic Systems Analysis of Halomonas smyrnensis AAD6(T)(WILEY, 2013) TOKSOY ÖNER, EBRU; Ates, Ozlem; Arga, Kazim Y.; Oner, Ebru ToksoyHalomonas smyrnensis AAD(T) is a halophilic, gram-negative bacterium that can efficiently produce levan from sucrose as carbon source via levansucrase activity. However, systems-based approaches are required to further enhance its metabolic performance for industrial application. As an important step toward this goal, the genome-scale metabolic network of Chromohalobacter salexigens DSM3043, which is considered a model organism for halophilic bacteria, has been reconstructed based on its genome annotation, physiological information, and biochemical information. In the present work, the genome-scale metabolic network of C. salexigens was recruited, and refined via integration of the available biochemical, physiological, and phenotypic features of H. smyrnensis AAD6(T). The generic metabolic model, which comprises 1,393 metabolites and 1,108 reactions, was then systematically analyzed in silico using constraints-based simulations. To elucidate the relationship between levan biosynthesis and other metabolic processes, an enzyme-graph representation of the metabolic network and a graph decomposition technique were employed. Using the concept of control effective fluxes, significant links between several metabolic processes and levan biosynthesis were estimated. The major finding was the elucidation of the stimulatory effect of mannitol on levan biosynthesis, which was further verified experimentally via supplementation of mannitol to the fermentation medium. The optimal concentration of 30 g/L mannitol supplemented to the 50 g/L sucrose-based medium resulted in a twofold increase in levan production in parallel with increased sucrose hydrolysis rate, accumulated extracellular glucose, and decreased fructose uptake rate. (c) 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:1386-1397, 2013Publication Metadata only Toward Precision Oncology in Glioblastoma with a Personalized Cancer Genome Reporting Tool and Genetic Changes Identified by Whole Exome Sequencing(2023-09-01) ERDOĞAN, ONUR; ERZİK, CAN; ARĞA, KAZIM YALÇIN; BAYRAKLI, FATİH; ERDOĞAN O., Özkaya Ş. Ç., ERZİK C., Bilguvar K., ARGA K. Y., BAYRAKLI F.Precision/personalized medicine in oncology has two key pillars: molecular profiling of the tumors and personalized reporting of the results in ways that are clinically contextualized and triangulated. Moreover, neurosurgery as a field stands to benefit from precision/personalized medicine and new tools for reporting of the molecular findings. In this context, glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor prognosis. Precision/personalized medicine has emerged as a promising approach for personalized therapy in GBM. In this study, we performed whole exome sequencing of tumor tissue samples from six newly diagnosed GBM patients and matched nontumor control samples. We report here the genetic alterations identified in the tumors, including single nucleotide variations, insertions or deletions (indels), and copy number variations, and attendant mutational signatures. Additionally, using a personalized cancer genome-reporting tool, we linked genomic information to potential therapeutic targets and treatment options for each patient. Our findings revealed heterogeneity in genetic alterations and identified targetable pathways, such as the PI3K/AKT/mTOR pathway. This study demonstrates the prospects of precision/personalized medicine in GBM specifically, and neurosurgical oncology more generally, including the potential for genomic profiling coupled with personalized cancer genome reporting. Further research and larger studies are warranted to validate these findings and advance the treatment options and outcomes for patients with GBM.Publication Metadata only Genome reprogramming in Saccharomyces cerevisiae upon nonylphenol exposure(AMER PHYSIOLOGICAL SOC, 2017) MERTOĞLU, BÜLENT; Bereketoglu, Ceyhun; Arga, Kazim Yalcin; Eraslan, Serpil; Mertoglu, BulentBioaccumulative environmental estrogen, nonylphenol (NP; 4-nonylphenol), is widely used as a nonionic surfactant and can affect human health. Since genomes of Saccharomyces cerevisiae and higher eukaryotes share many structural and functional similarities, we investigated subcellular effects of NP on S.cerevisiae BY4742 cells by analyzing genome-wide transcriptional profiles. We examined effects of low (1 mg/l; <15% cell number reduction) and high (5 mg/l; > 65% cell number reduction) inhibitory concentration exposures for 120 or 180 min. After 120 and 180 min of 1 mg/l NP exposure, 187 (63 downregulated, 124 upregulated) and 103 genes (56 downregulated, 47 upregulated), respectively, were differentially expressed. Similarly, 678 (168 repressed, 510 induced) and 688 genes (215 repressed, 473 induced) were differentially expressed in cells exposed to 5 mg/l NP for 120 and 180 min, respectively. Only 15 downregulated and 63 upregulated genes were common between low and high NP inhibitory concentration exposure for 120 min, whereas 16 downregulated and 31 upregulated genes were common after the 180-min exposure. Several processes/pathways were prominently affected by either low or high inhibitory concentration exposure, while certain processes were affected by both inhibitory concentrations, including ion transport, response to chemicals, transmembrane transport, cellular amino acids, and carbohydrate metabolism. While minimal expression changes were observed with low inhibitory concentration exposure, 5 mg/l NP treatment induced substantial expression changes in genes involved in oxidative phosphorylation, cell wall biogenesis, ribosomal biogenesis, and RNA processing, and encoding heat shock proteins and ubiquitin-conjugating enzymes. Collectively, these results provide considerable information on effects of NP at the molecular level.Publication Metadata only Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis(MARY ANN LIEBERT, INC, 2016) KAZAN, DİLEK; Kori, Medi; Aydin, Busra; Unal, Semra; Arga, Kazim Yalcin; Kazan, DilekNeurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) lack robust diagnostics and prognostic biomarkers. Metabolomics is a postgenomics field that offers fresh insights for biomarkers of common complex as well as rare diseases. Using data on metabolite-disease associations published in the previous decade (2006-2016) in PubMed, ScienceDirect, Scopus, and Web of Science, we identified 101 metabolites as putative biomarkers for these three neurodegenerative diseases. Notably, uric acid, choline, creatine, L-glutamine, alanine, creatinine, and N-acetyl-L-aspartate were the shared metabolite signatures among the three diseases. The disease-metabolite-pathway associations pointed out the importance of membrane transport (through ATP binding cassette transporters), particularly of arginine and proline amino acids in all three neurodegenerative diseases. When disease-specific and common metabolic pathways were queried by using the pathway enrichment analyses, we found that alanine, aspartate, glutamate, and purine metabolism might act as alternative pathways to overcome inadequate glucose supply and energy crisis in neurodegeneration. These observations underscore the importance of metabolite-based biomarker research in deciphering the elusive pathophysiology of neurodegenerative diseases. Future research investments in metabolomics of complex diseases might provide new insights on AD, PD, and ALS that continue to place a significant burden on global health.Publication Metadata only Precision oncology: an ensembled machine learning approach to ıdentify a candidate mrna panel for stratification of patients with breast cancer(2022-08-01) AĞAOĞLU, MUSTAFA; ARĞA, KAZIM YALÇIN; Kurt, Fırat; Kurt F., AĞAOĞLU M., ARĞA K. Y.The rise of machine learning (ML) has recently buttressed the efforts for big data-driven precision oncology. This study used ensemble ML for precision oncology in breast cancer, which is one of the most common malignancies worldwide with marked heterogeneity of the underlying molecular mechanisms. We analyzed clinical and RNA-seq data from The Cancer Genome Atlas (TCGA) (844 patients with breast cancer and 113 healthy individuals) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (1784 patients with breast cancer and 202 healthy individuals). We evaluated six algorithms in the context of ensemble modeling and identified a candidate mRNA diagnostic panel that can differentiate patients from healthy controls, and stratify breast cancer into molecular subtypes. The ensemble model included 50 mRNAs and displayed 82.55% accuracy, 79.22% specificity, and 84.55% sensitivity in stratifying patients into molecular subtypes in TCGA cohort. Its performance was markedly higher, however, in distinguishing the basal, LumB, and Her2+ breast cancer subtypes from healthy individuals. In overall survival analysis, the mRNA panel showed a hazard ratio of 2.25 (p = 5 x 10(-7)) for breast cancer and was significantly associated with molecular pathways related to carcinogenesis. In conclusion, an ensemble ML approach, including 50 mRNAs, was able to stratify patients with different breast cancer subtypes and differentiate them from healthy individuals. Future prospective studies in large samples with deep phenotyping can help advance the ensemble ML approaches in breast cancer. Advanced ML methods such as ensemble learning are timely additions to the precision oncology research toolbox.Publication Metadata only Omics of Selenium Biology: A Prospective Study of Plasma Proteome Network Before and After Selenized-Yeast Supplementation in Healthy Men(MARY ANN LIEBERT, INC, 2016) ARĞA, KAZIM YALÇIN; Sinha, Indu; Karagoz, Kubra; Fogle, Rachel L.; Hollenbeak, Christopher S.; Zea, Arnold H.; Arga, Kazim Y.; Stanley, Anne E.; Hawkes, Wayne C.; Sinha, RaghuLow selenium levels have been linked to a higher incidence of cancer and other diseases, including Keshan, Chagas, and Kashin-Beck, and insulin resistance. Additionally, muscle and cardiovascular disorders, immune dysfunction, cancer, neurological disorders, and endocrine function have been associated with mutations in genes encoding for selenoproteins. Selenium biology is complex, and a systems biology approach to study global metabolomics, genomics, and/or proteomics may provide important clues to examining selenium-responsive markers in circulation. In the current investigation, we applied a global proteomics approach on plasma samples collected from a previously conducted, double-blinded placebo controlled clinical study, where men were supplemented with selenized-yeast (Se-Yeast; 300g/day, 3.8mol/day) or placebo-yeast for 48 weeks. Proteomic analysis was performed by iTRAQ on 8 plasma samples from each arm at baseline and 48 weeks. A total of 161 plasma proteins were identified in both arms. Twenty-two proteins were significantly altered following Se-Yeast supplementation and thirteen proteins were significantly changed after placebo-yeast supplementation in healthy men. The differentially expressed proteins were involved in complement and coagulation pathways, immune functions, lipid metabolism, and insulin resistance. Reconstruction and analysis of protein-protein interaction network around selected proteins revealed several hub proteins. One of the interactions suggested by our analysis, PHLD-APOA4, which is involved in insulin resistance, was subsequently validated by Western blot analysis. Our systems approach illustrates a viable platform for investigating responsive proteomic profile in before and after' condition following Se-Yeast supplementation. The nature of proteins identified suggests that selenium may play an important role in complement and coagulation pathways, and insulin resistance.Publication Metadata only Combining various heterogeneous biological features to obtain a highly reliable almost complete protein interaction network of yeast(ELSEVIER SCIENCE BV, 2012) ARĞA, KAZIM YALÇIN; Karagoz, Kubra; Arga, Kazim YalcinPublication Metadata only Triple Negative Breast Cancer: A Multi-Omics Network Discovery Strategy for Candidate Targets and Driving Pathways(MARY ANN LIEBERT, INC, 2015) ARĞA, KAZIM YALÇIN; Karagoz, Kubra; Sinha, Raghu; Arga, Kazim YalcinTriple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.Publication Metadata only COVID-19 and the Futures of Machine Learning(MARY ANN LIEBERT, INC, 2020) ARĞA, KAZIM YALÇIN; Arga, Kazim YalcinPublication Metadata only Genomic analysis of Brevibacillus thermoruber 423 reveals its biotechnological and industrial potential(SPRINGER, 2015) TOKSOY ÖNER, EBRU; Yildiz, Songul Yasar; Radchenkova, Nadja; Arga, Kazim Yalcin; Kambourova, Margarita; Oner, Ebru ToksoyBrevibacillus thermoruber 423 is a Gram-positive, motile, red-pigmented, spore-forming, aerobic, and thermophilic bacterium that is known to produce high levels of exopolysaccharide (EPS) with many potential uses in food, feed, cosmetics, and pharmaceutical and chemical industries. This bacterium not only is among the limited number of reported thermophilic EPS producers but also exceeds other thermophilic producers in light of the high level of polymer synthesis. By a systems-based approach, whole-genome analysis of this bacterium was performed to gain more insight about the biological mechanisms and whole-genome organization of thermophilic EPS producers and hence to develop rational strategies for the genetic and metabolic optimization of EPS production. Also with this study, the first genome analysis was performed on a thermophilic Brevibacillus species. Essential genes associated with EPS biosynthesis were detected by genome annotation, and together with experimental evidences, a hypothetical mechanism for EPS biosynthesis was generated. B. thermoruber 423 was found to have many potential applications in biotechnology and industry because of its capacity to utilize xylose and to produce EPS, isoprenoids, ethanol/butanol, lipases, proteases, cellulase, and glucoamylase enzymes as well as its resistance to arsenic.